A complete and up-to-date discussion of optimal split plot and split block designs
Variations on Split Plot and Split Block Experiment Designs provides a comprehensive treatment of the design and analysis of two types of trials that are extremely popular in practice and play an integral part in the screening of applied experimental designs--split plot and split block experiments. Illustrated with numerous examples, this book presents a theoretical background and provides two and three error terms, a thorough review of the recent work in the area of split plot and split blocked experiments, and a number of significant results.
Written by renowned specialists in the field, this book features: * Discussions of non-standard designs in addition to coverage of split block and split plot designs * Two chapters on combining split plot and split block designs and missing observations, which are unique to this book and to the field of study * SAS? commands spread throughout the book, which allow readers to bypass tedious computation and reveal startling observations * Detailed formulae and thorough remarks at the end of each chapter * Extensive data sets, which are posted on the book's FTP site
The design and analysis approach advocated in Variations on Split Plot and Split Block Experiment Designs is essential in creating tailor-made experiments for applied statisticians from industry, medicine, agriculture, chemistry, and other fields of study.
About the Author
WALTER T. FEDERER, PHD, is Liberty Hyde Bailey Professor of Biological Statistics, Emeritus, at Cornell University. He has held numerous posts as editor, secretary, and president of various journals and societies, and he is also a Fellow or member of more than a dozen local, regional, national, and international associations. Dr. Federer received the Honor Alumnus Achievement Award and Honored Alumnus Award from Colorado State University and the Distinguished Service in Agriculture Award from Kansas State University.
FREEDOM KING, PHD, is Teaching Support Specialist in the Department of Biological Statistics and Computational Biology at Cornell University. His areas of interest include generalized linear mixed models, experimental designs, categoricaldata analysis, and statistical computing with an emphasis on work sales through SAS® programming techniques.
